Ticket Resale
Phillip Leslie
Stanford University & NBER
Alan Sorensen
Stanford University & NBER
June 2007
Leslie and Sorensen
Ticket Resale
Motivation
Determinants of resale activity?
General underpricing
Unpriced seat quality
Late arrivals
Schedule conflicts
Leslie and Sorensen
Ticket Resale
Motivation
Determinants of resale activity?
General underpricing
Unpriced seat quality
Late arrivals
Schedule conflicts
Welfare consequences of resale?
Consumer surplus
Producer surplus
Transaction costs?
Anti-scalping laws?
Leslie and Sorensen
Ticket Resale
Data description
Ticketmaster data (primary market sales)
372 concerts from summer of 2004
32 major artists
5.9 million tickets
$282 million in revenue
Leslie and Sorensen
Ticket Resale
Data description
Ticketmaster data (primary market sales)
372 concerts from summer of 2004
32 major artists
5.9 million tickets
$282 million in revenue
What we observe:
If and when a ticket was sold
Price (including fees)
Seat quality (i.e., order in which tickets were sold)
Leslie and Sorensen
Ticket Resale
Data description, continued
eBay and Stubhub (secondary market sales)
139,290 resold tickets (95% eBay)
$14.9 million in revenue
Leslie and Sorensen
Ticket Resale
Data description, continued
eBay and Stubhub (secondary market sales)
139,290 resold tickets (95% eBay)
$14.9 million in revenue
What we observe:
Resale price (i.e., winning bid plus shipping)
Section and row (usually)
Seller ID
Seller type (broker or non-broker)
Date and time of sale
Leslie and Sorensen
Ticket Resale
Summary statistics (across events; N=372)
.10
Primary Market:
# Tickets sold
# comps
Total Revenue
Capacity
Cap. Utilization
Average price
Max price
# price levels
Week 1 sales (%)
Resale Market:
# Tickets resold
Resale revenue
% resold
% revenue
Average price
Max price
Average markup
Median % markup
Percentiles
.50
.90
4,011
80
200.8
7,387
0.47
35.83
51.90
2
0.20
10,279
536
622.1
16,264
0.87
52.03
77.50
4
0.47
19,159
1,642
1,452.8
24,255
1.00
84.54
150.29
8
0.78
65
4,448.8
0.01
0.02
64.60
144.00
3.22
-0.03
242
21,945.2
0.02
0.04
97.49
286.37
31.58
0.37
833
96,041.3
0.06
0.09
138.09
610.00
59.99
0.94
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Brokers tend to earn
higher resale markups
Leslie and Sorensen
Ticket Resale
27% of broker resales have
markups of 100% or more
(15% for consumers)
Leslie and Sorensen
Ticket Resale
Consumers more likely
than brokers to resell
below face value
Leslie and Sorensen
Ticket Resale
A two-period model
Period 1 (Primary Market):
Potential buyers arrive in a random sequence
Seats offered in “best available” order
Prices are taken as given
Leslie and Sorensen
Ticket Resale
A two-period model
Period 1 (Primary Market):
Potential buyers arrive in a random sequence
Seats offered in “best available” order
Prices are taken as given
Period 2 (Resale Market):
Ticket-holders can sell their tickets if they choose
Some ticket-holders have schedule conflicts (forced to resell)
Resale prices are endogenous
Note: Only one transaction per person per period
Leslie and Sorensen
Ticket Resale
Decisions and payoffs
In period 1, consumers decide whether to buy or wait. In period 2,
ticketholders either resell or consume; non-ticketholders buy or not.
Brokers either buy or not in period 1. If they buy, they always
resell in period 2.
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
CONSUMERS
Schedule
conflict
Resell
Resell
Buy
No schedule
conflict
Consume
Do nothing
Wait
Schedule
conflict
Buy
No schedule
conflict
Don’t buy
Leslie and Sorensen
Ticket Resale
BROKERS
Sell
Buy
Not sell
Not buy
Leslie and Sorensen
Ticket Resale
BROKERS
Sell
Buy
Not sell
Not buy
Leslie and Sorensen
Ticket Resale
BROKERS
Sell
Buy
Not sell
Not buy
Leslie and Sorensen
Ticket Resale
BROKERS
Sell
Buy
Not sell
Not buy
Leslie and Sorensen
Ticket Resale
Clearing the resale market
Sequence of second-price auctions:
Start with highest quality owned ticket
Randomly draw K bidders and conduct a second-price auction
Transaction occurs only if offer price exceeds buyer’s
reservation price
Go to the next-highest quality owned ticket, and repeat
Note: random participation allows us to fit the observed variance
in resale prices
Leslie and Sorensen
Ticket Resale
Rational expectations
Buyers’ primary market decisions must be optimal given their
expectations about the resale market
Leslie and Sorensen
Ticket Resale
Rational expectations
Buyers’ primary market decisions must be optimal given their
expectations about the resale market
And those expectations must be correct (on average) given
optimal behavior in the primary market
Leslie and Sorensen
Ticket Resale
Rational expectations
Buyers’ primary market decisions must be optimal given their
expectations about the resale market
And those expectations must be correct (on average) given
optimal behavior in the primary market
First-period value function:
Z
V (ωi , νj , bi ) = U(ωi , νj , bi |z, s)dGz (z)dGs (s)
(Uncertainty is with respect to arrival sequence (z) and
schedule conflicts (s))
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Starting with an initial conjecture, V̂0 :
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Starting with an initial conjecture, V̂0 :
1
For a given arrival sequence, compute primary and secondary
market allocations that result from optimal decisions based on
V̂0
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Starting with an initial conjecture, V̂0 :
1
For a given arrival sequence, compute primary and secondary
market allocations that result from optimal decisions based on
V̂0
2
Repeat for a large number of arrival sequences
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Starting with an initial conjecture, V̂0 :
1
For a given arrival sequence, compute primary and secondary
market allocations that result from optimal decisions based on
V̂0
2
Repeat for a large number of arrival sequences
3
Regress realized final utilities on ω, ν, b to construct a new
estimate of the value function: V̂1
Leslie and Sorensen
Ticket Resale
Solving the model
Approximate the value function parametrically: V̂ (ω, ν, b|α)
Starting with an initial conjecture, V̂0 :
1
For a given arrival sequence, compute primary and secondary
market allocations that result from optimal decisions based on
V̂0
2
Repeat for a large number of arrival sequences
3
Regress realized final utilities on ω, ν, b to construct a new
estimate of the value function: V̂1
4
Iterate until V̂ converges to a fixed point
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Few bidders Æ
More price variance
Leslie and Sorensen
Ticket Resale
Many bidders Æ
Less price variance
Leslie and Sorensen
Ticket Resale
Positive transaction costs Æ
Few resales with low markups
Leslie and Sorensen
Ticket Resale
Zero transaction costs Æ
Many resales with low markups
Leslie and Sorensen
Ticket Resale
Leslie and Sorensen
Ticket Resale
Brokers have lower transaction costs
Æ Willing to sell at lower markups
Leslie and Sorensen
Ticket Resale
Brokers may lead to fewer primary
market sales
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market?
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market? (set τ c = τ b = 0)
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market? (set τ c = τ b = 0)
What if we could reduce transaction costs?
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market? (set τ c = τ b = 0)
What if we could reduce transaction costs? (lower τ c or τ b )
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market? (set τ c = τ b = 0)
What if we could reduce transaction costs? (lower τ c or τ b )
What if we eliminate brokers?
Leslie and Sorensen
Ticket Resale
Where we go from here
Estimate the model (!)
Counterfactual analyses:
What if there were no resale market? (set τ c = τ b = 0)
What if we could reduce transaction costs? (lower τ c or τ b )
What if we eliminate brokers? (set bi = 0 for all i)
Leslie and Sorensen
Ticket Resale